The rapid development of noninvasive brain imaging technologies has opened new horizons in analyzing and studying the anatomy and function of the body. In an example, progress in accessing brain injury and exploring brain anatomy has been made using magnetic resonance (MR) imaging. The advances in brain MR imaging have also provided data with an increasingly high level of quality. The analysis of the MR datasets has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is often time-consuming and prone to errors. More recently, computerized methods for MR image segmentation, registration, and visualization have been extensively used to assist doctors and clinicians in qualitative diagnosis.
The medical imaging environment is highly diverse in terms of data acquisition, contrast or resolution. Brain Segmentation for instance, is a standard preprocessing step for neuroimaging applications, often used as a prerequisite for anomaly detection, tissue segmentation, and morphometry applications. Brain MR segmentation is an essential task in many clinical applications because it influences the outcome of the entire analysis. This is because different processing steps rely on accurate segmentation of anatomical regions. For example, MR segmentation is commonly used for measuring and visualizing different brain structures, for delineating lesions, for analyzing brain development, and for image-guided interventions and surgical planning. Each clinical application may require or use different resolutions or dimensions. To perform the segmentation task, each clinical application may thus require a dedicated segmentation application or network.
Automating brain segmentation is challenging due to the sheer amount of variations of brain shapes and sizes as well as variation in imaging. Protocol differences in MR acquisition may lead to variations in image resolution. In an example, a first medical imaging scan may use a first resolution while a second medical imaging scan may use a second resolution. The resolutions or dimensions of the resulting images or volumes may be different due to the intended use of the imaging, making automated brain segmentation less reliable.